Server device configurations based on machine learning

ABSTRACT

A system, a medium, and a method are provided to exchange data packets over a communications network and perform machine learning operations. A network server device receives account data from client devices that correspond to account profiles. An account engine of the network server device segments the account profiles into profile groups based on a respective balance associated with each account profile. The account engine determines target accounts from profile groups based on behavioral data. Further, data processing components of the network server device determine a method of contact for each target account. The data processing components determine a respective time to communicate with a respective device for each target account. Further, communication components of the network server device initiate communications to the respective devices at the respective times for each target account.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.14/814,441, filed Jul. 30, 2015, which is incorporated by reference inits entirety.

TECHNICAL FIELD

This disclosure generally relates to electronic communications systems,and more particularly, systems configured to exchange data packets overa communications network.

BACKGROUND

Various types of electronic communication systems may include a serverdevice that exchanges data packets with multiple other types ofcomputing devices, possibly over a communications network. For example,the server device may exchange data packets with desktop computers,laptop computers, tablet computers, and/or smartphones. As these devicesbecome increasingly more prevalent in the modern world, it is common forindividuals to use such devices as part of their daily lives. Forexample, a user may make multiple purchases using a single mobiledevice, such as a smartphone. Further, a merchant may similarly sellnumerous items or services using a tablet computer and/or apoint-of-sale (POS) device. As such, the ability to seamlessly purchaseand sell items or services may be convenient, possibly enabling users tobuy items or services from merchants at virtually any location and atany time.

Yet, a number of issues may also arise. In some instances, buyers maypurchase and/or make commitments to purchase items that they cannotafford. Further, merchants may commit to selling items that they do nothave in stock and/or promise to sell items at prices that amount tolosses. As such, various parties including users, merchants, buyers,and/or sellers are often found with accounts that have low balances,zero balances, and in many instances, negative or overdue balances. Invarious such circumstances, it may be difficult to urge such users topay off their negative or overdue balances.

Further, it may be difficult to contact or reach such users to informthem regarding their accounts. Even after agents make thousands ofattempts to contact such users, a small number of users actually can bereached. For example, users may not answer their calls and/or they mayignore their smartphones based on an identification (or lack ofidentification) of the party attempting to reach them or possibly basedon not being able to identify or recognize the party attempting to reachthem. Yet further, even a smaller number of users actually reached mayresult in the user paying off their negative or overdue balances.

In addition, it is also a concern that users with negative or overdueaccount balances may continue making commitments with other users, andpossibly with other more responsible users with positive or up-to-datebalances. For example, users with negative account balances may open newaccounts and continue making obligations or commitments to the moreresponsible users to purchase items they cannot afford and/or promise tosell items that they do not have in stock, among other poor choices asdescribed above.

Thus, there is much need for technological advancements to improvesystems that attempt to communicate with users who have low, zero,negative, and/or overdue account balances and collect from those users.In particular, there is need for such improvements to help reach users,and contain or prevent them from creating new accounts and continuing toperform activities under new accounts.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary system, according to anembodiment;

FIG. 2A illustrates an exemplary server device configured to support aset of trays, according to an embodiment;

FIG. 2B illustrates an exemplary tray configured to support one or morecomponents, according to an embodiment;

FIG. 3A illustrates an exemplary system with a number of client devices,according to an embodiment;

FIG. 3B illustrates data packets and a number of target accounts,according to an embodiment;

FIG. 3C is a flowchart of an exemplary method, according to anembodiment;

FIG. 3D illustrates data packets and score data of a number of targetaccounts, according to an embodiment;

FIG. 3E illustrates data packets and a ranking of a number of targetaccounts, according to an embodiment;

FIG. 4 illustrates an exemplary input/output (I/O) interface of a clientdevice, according to an embodiment;

FIG. 5 is an exemplary system, according to an embodiment;

FIG. 6 is a flowchart of an exemplary method, according to anembodiment; and

FIGS. 7A and 7B illustrate an exemplary wearable computing device,according to an embodiment.

Embodiments of the present disclosure and their advantages may beunderstood by referring to the detailed description that follows. Itshould be appreciated that like reference numerals are used to identifyelements illustrated in one or more of the figures, where the figuresillustrate various examples for purposes of illustration and explanationrelated to the embodiments of the present disclosure and not forpurposes of limitation.

DETAILED DESCRIPTION

Example embodiments herein describe the performance of machine learningoperations to communication with and collection from users who have low,negative, and/or overdue account balances. In particular, the machinelearning operations may be performed to help systems reach users, andpossibly to further contain and/or prevent further activities, such asactivating or creating new accounts and performing activities under thenewly created accounts, for example. Notably, collecting payments fromusers with negative and/or overdue accounts may be important for serviceproviders. Further, collecting such payments may also help to improvethe experiences and/or perspectives of other, possibly more responsibleusers who may be interacting with the users who have the negative and/oroverdue account balances. For example, the service provider may want toensure that the more responsible users are satisfied interacting withother users who also have accounts maintained by the service provider.

In some embodiments, a system may include a network server device of aservice or payment provider, such as PayPal, Inc. of San Jose, Calif.,USA, configured to perform machine learning operations. For example, thenetwork server device may be configured to contact and/or reach userswith negative and/or overdue accounts. In particular, the network serverdevice may be configured to segment a number of accounts or accountprofiles into different account groups based on respective balances(e.g., negative and/or overdue balances) associated with each of theaccounts. Further, the network server device may be configured todetermine target accounts from the different account groups, where thetarget accounts may identify the users of the target accounts thatshould be pursued or contacted. Yet further, the network server devicesmay be configured to perform machine learning operations, such asdetermining various times to contact the users of the target accountsand ways to contact the users based on behavioral data retrieved. Assuch, the network server device may be used to initiate communicationswith the mobile devices of the targeted users to remind and/or urge thetargeted users to partially or fully pay off their negative accountbalances.

In some embodiments, a system configured to exchange data packets andperform machine learning operations may include a network server device,such as those described above. The network server may be configured toreceive account data from numerous client devices, where the clientdevices correspond to multiple account profiles of users. An accountengine of the network server device may segment the number of accountprofiles into a number of profile groups. For example, the accountengine may segment account profiles described, indicated, and/or labeledas “slacker,” “unreliable,” “unpredictable,” “troubled,” and/or“fraudster.” The behavioral data for each target account may alsoindicate the number of late payments, missed payments, and/or rejectedpayments made under each target account, possibly by a targeted user.

The account engine may also determine a number of target accounts fromthe number of profile groups based on behavioral data retrieved by thenetwork server device, where the behavioral data indicates variousactivities (e.g., purchases, sales, and/or transactions) of the targetaccounts. One or more data processing components of the network serverdevice may determine a method of contact for each target account basedon the behavioral data. Further, the one or more data processingcomponents may determine a respective time to communicate with arespective device of each target account based on the methods of contactdetermined. Thus, one or more communication components of the networkserver device may initiate communications to the respective devices atthe respective times for each target account, possibly to remind and/orurge the targeted users to partially or fully pay off their accountbalances.

In some embodiments, the behavioral data may also include location dataindicative of locations of the respective device of each target account.For example, the location data may include global positioning system(GPS) data, beacon data, WI-FI signal strength data, base station data,triangulation data between the respective device and two or more otherclient devices, and/or sensor data retrieved by the number of clientdevices, where the sensor data further indicates the locations of therespective device. The sensor data may also include movement data (e.g.,velocity and/or acceleration data), temperature data, altitude data,directional data, orientation data, and/or other types of data todetermine the location of the respective device of each target account.

The behavioral data may also include payment data indicative of times ofpayments made or missed for at least one of the target accounts. Thebehavioral data may also include pattern data indicative of patterns ofpayments made or missed for at least one of the target accounts. Thebehavioral data may also include recurring payment data indicative ofone or more recurring payments for at least one of the target accounts,such as automated or scheduled payment plans. Further, the behavioraldata further may include peer data (e.g., data of other related orlinked accounts) and/or social media data indicative of methods/ways tocontact peer accounts associated with the number of target accounts. Forexample, the social media data may include phone numbers and/or emailaddresses of peer accounts or friends associated with the targetaccounts. Thus, the one or more data processing components may determinethe methods of contact, e.g., phone numbers and/or email addresses, foreach target account based on the social media networks and related dataincluding methods/ways to contact peer accounts.

In some embodiments, the behavioral data associated with the targetaccounts indicates times associated with deposits submitted to thetarget accounts. For example, the behavior data may indicate the date,hour, and/or time or time frame when the targeted user (or another useron behalf of the targeted user) submits a deposit to their targetedaccount. As such, the one or more data processing components maydetermine the respective times to communicate with the respectivedevices for each target account, possibly based on the times associatedwith the deposits submitted. By communicating with the respectivedevices shortly after deposits are submitted to the corresponding targetaccounts, the probability of the targeted user paying off the targetedaccount balance may be increased.

In some embodiments, seller data may be used to determine when and howto contact a seller that has a low, zero, and/or negative balance. Forexample, consider a target account of a seller identified by the networkserver. The network server may determine that items are listed and soldunder the target account, even where funds are not necessarily depositedinto any account. For example, the activities or transactions may becompleted in cash. As such, the network server may determine that thetime to contact the targeted seller would be during or after the one ormore activities, possibly where the targeted user receives cash.Further, the network server may determine the times to contact thetargeted seller based on when the targeted seller may be expecting abusy selling period. For example, the targeted seller may be offering orselling “hot” or popular items. Further, the targeted seller may beselling items during traditional holidays (e.g., Christmas) and/ornon-traditional (e.g., baseball opening day for a sports seller) inparticular locations, as disclosed herein.

It should be noted that based on network server configurations, lowand/or zero account balances may be identified in addition to thenegative balances. In some instances, the identification of low and/orzero account balances may proactively enable the network server todetermine or identify the target accounts with such balances. As such,the network server may proactively contact the users of such targetaccounts to prevent their accounts from becoming negative. In someinstances, proactively informing such users may be effective in ensuringthat users have positive balances in their respective accounts. Forexample, the network server may notify such users with possible alarmsor messages such as, “Your Account Balance is Approaching Zero!” or“Your Account Balance is Low!”

In some embodiments, the one or more data processing components maydetermine a probability score for each target account based on aprobability that the respective target account receives a payment,possibly to pay off the account balance. The one or more data processingcomponents may also determine a method score for each target accountbased on the determined method of contact for each target account. Forexample, the method score may be higher or stronger for an identifiedphone number as opposed to an email address. The one or more dataprocessing components may also determine a time score for each targetaccount based on the respective times determined to communicate with therespective devices. For example, the time score may be higher orstronger based on additional times identified when a targeted user maybe available or reached. The one or more communication components mayalso initiate the communications to the respective devices based on theprobability score, the method score, the time score, and/or anycombination of the scores.

In some embodiments, the one or more communication components mayinitiate the communications with at least one of an email communication,a text or short message service (SMS) communication, and/or a telephoniccommunication to the respective devices at the respective times,possibly based on the probability score, the method score, the timescore, and/or a combination of the scores.

FIG. 1 is a block diagram of an example system 100, according to anembodiment. The system 100 may be configured to exchange data packetsover a communications system and perform machine learning operations. Asshown, the system 100 includes multiple computing devices, such as anetwork server device 102, a client device 104, and a client device 106,among other possible computing devices. The server device 102 may beconfigured to support, operate, and/or manage numerous accounts or useraccounts. The system 100 may operate with more or less computing devicesthan those shown in FIG. 1, where each device may be configured tocommunicate over a communication network 108. As shown, thecommunication network 108 may include a data network 108A and a cellularnetwork 108B. Thus, the server device 102, the client device 104, and/orthe client device 106 may each be configured to communicate over thecommunication network 108.

The network server device 102 may be configured to perform variousoperations in accordance with this disclosure and the accompanyingfigures. In some embodiments, the network server device 102 may beconfigured to receive account data, e.g., data/data packets 122 and/or124, from a number of client devices 104 and/or 106, where the clientdevices 104 and/or 106 correspond to a number of account profiles. Thenetwork server device 102 may segment the number of account profilesinto a number of profile groups based on a respective balance associatedwith each account profile. The network server device 102 may alsodetermine a number of target accounts from the number of profile groupsbased on behavioral data retrieved from the account data 122 and/or 124.The network server device 102 may determine a method of contact for eachtarget account based on the behavioral data. The network server device102 may also determine a respective time to communicate with arespective device 104 and/or 106 for each target account based at leaston the methods of contact. The network server device 102 may alsoinitiate communications to the respective devices 104 and/or 106 at therespective times for each target account.

For example, the network server device 102 may determine the respectivetime to communicate with a respective device 104 and/or 106 for eachtarget account, where the respective device 104 and/or 106 may includeor take the form a smartphone, a laptop, a tablet computer, a wearablecomputing device, a head-mountable display, a smart watch, and/or otherdevices associated with the target account, e.g., registered under thetarget account and/or owned or possessed by the targeted user. Theaccount data, e.g., data packets 122 and/or 124, may be accessible viaprotocols such as Transmission Control Protocol/Internet Protocol(TCP/IP). In various embodiments, each of the data packets 122 and 124may include 1,000 to 1,500 bytes, among other possible data ranges.

In some embodiments, the network server device 102 may take a variety offorms. The network server device 102 may take the form of a stand-aloneand/or an enterprise-class server device, and/or a server deviceimplementing one or more operating systems such as client- and/orserver-based operating systems. Further, the network server device 102may include multiple components, including, for example, an accountengine 112, a contact data processing component 114, a time dataprocessing component 116, a communication component and/or interface118, and a memory component 120, any of which may be communicativelylinked via a system bus, network, or other connection mechanism 122.

The account engine 112, the contact data processing component 114,and/or the time data processing component 116 may each take the form ofa multi-purpose processor, a microprocessor, a special purposeprocessor, a digital signal processor (DSP), an application specificintegrated circuit (ASIC), a programmable system on chip (PSOC),field-programmable gate array (FPGA), and/or other types of processingcomponents. For example, the account engine 112 may take the form of adedicated processing component configured to segment the accountprofiles into a number of profile groups based on a respective balanceassociated with each account profile. The account engine 112 may alsodetermine a number of target accounts from the number of profile groupsbased on behavioral data retrieved from the account data 122 and/or 124.The data processing components 114 and/or 116 may each take the form ofdedicated processing components configured to determine a method ofcontact for each target account based on the behavioral data and alsodetermine a respective time to communicate with a respective device foreach target account based on the methods of contact.

The communication component or interface 118 may take a variety of formsand may be configured to allow the network server device 102 tocommunicate with one or more devices, such as client device 104 and/or106, according to a number of protocols. For example, the communicationcomponent 118 may include a transceiver that enables the network serverdevice 102 to communicate with the client devices 104 and/or 106 via thecommunication network 108. Further, the communication component 118 maytake the form of a wired interface, such as an Ethernet interface. Yetfurther, the communication component 118 may take the form of a wirelessinterface, such as a cellular or microcell interface, a WI-FI interface,another short-range, point-to-multipoint voice and/or data transfercommunication interface, such as BLUETOOTH. In some instances, thecommunication component 118 may send/receive data or data packetsto/from client devices 104 and/or 106.

The memory component 120 may include one or more of volatile,non-volatile, and/or replaceable storage components, such as magnetic,optical, or flash storage, and may be integrated in whole or in partwith the account engine 112, and/or the data processing components 114and/or 116. Further, the memory component 120 may include or take theform of a non-transitory computer-readable storage medium, having storedthereon computer-readable instructions (e.g., compiled or non-compiledprogram logic and/or machine code) that, when executed by the networkserver device 102, cause the network server device 102 to performmachine learning operations, such as those described in this disclosureand illustrated by the accompanying figures.

The client devices 104 and 106 may also be configured to perform avariety of operations such as those described in this disclosure andillustrated by the accompanying figures. In particular, client devices104 and 106 may be configured to exchange data/data packets 122 and/or124 with the network server device 102, that include account data,behavioral data possibly retrieved from the account data, location data,payment data, pattern data, recurring payment data, sensor data, peerdata, social media data, GPS data, beacon data, WI-FI data, base stationdata, triangulation data, sensor data, movement data, temperature data,and/or other types of data described above.

Client devices 104 and 106 may take a variety of forms, including, forexample, a personal computer (PC), a smartphone, a wearable computer, alaptop/tablet computer, a merchant device, a smart watch, ahead-mountable display, other types of wearable devices, and/or othertypes of computing devices configured to transmit and/or receive data,among other possibilities. The client devices 104 and 106 may includevarious components, including, for example, input/output (I/O)interfaces 130 and 140, communication interfaces 132 and 142, processors134 and 144, and data storages 136 and 146, respectively, all of whichmay be communicatively linked with each other via a system bus, network,or other connection mechanisms 138 and 148, respectively.

The I/O interfaces 130 and 140 may be configured to receive inputs from(and provide outputs to) one or more users of the client devices 104 and106. For example, the I/O interface 130 may enable a user to accesstheir account via the client device 104. Thus, the I/O interfaces 130and 140 may include input hardware such as a touchscreen, a touchsensitive panel, a microphone for receiving voice commands, a computermouse, a keyboard, and/or other input hardware. In addition, I/Ointerfaces 130 and 140 may include output hardware such as displays withthe touchscreens described above, a sound speaker, other audio outputmechanism, a haptic feedback system, and/or other output hardware.

In some embodiments, communication interfaces 132 and 142 may take avariety of forms and may be configured to allow client devices 104 and106, respectively, to communicate with one or more devices according toa number of protocols. For instance, communication interfaces 132 and142 may be configured to allow client devices 104 and 106, respectively,to communicate with the network server device 102 via the communicationnetwork 108. The processors 134 and 144 may include multi-purposeprocessors and/or special purpose processors such as those describedabove. Data storages 136 and 146 may include one or more volatile,non-volatile, removable, and/or non-removable storage components, andmay be integrated in whole or in part with processors 134 and 144,respectively. Further, data storages 136 and 146 may take the form ofnon-transitory computer-readable storage mediums, having stored thereoncomputer-readable instructions that, when executed by processors 134 and144, cause client devices 104 and 106 to perform operations,respectively, such as those described in this disclosure and illustratedby the accompanying figures.

In some embodiments, the communication network 108 may exchange dataamong the network server device 102, the client device 104, the clientdevice 106, and/or other computing devices as well. The communicationnetwork 108 may be a packet-switched network configured to providedigital networking communications and/or exchange data of various forms,content, type, and/or structure. The communication network 108 mayinclude a data network 108A with various sizes of communicationnetworks, such as a private and/or local area networks and/or wide areanetworks or the Internet. Further, the communication network 108 mayinclude a cellular network 108B with one or more base station networksand/or cellular networks of various sizes, possibly accessible byvarious devices 102, 104, and/or 106. The communication network 108 mayinclude network adapters, switches, routers, network nodes, basestations, microcells, and/or various buffers/queues to exchangedata/data packets 122 and/or 124.

For example, the communication network 108 may be configured to carrythe first data packet 122 and the second data packet 124 includingaccount data, behavioral data possibly retrieved from the account data,location data, payment data, pattern data, recurring payment data,sensor data, peer data, social media data, GPS data, beacon data, WI-FIdata, base station data, triangulation data, sensor data, movement data,temperature data, and/or other types of data described herein. Thecommunication network 108 may exchange data packets 122 and/or 124between the network server device 102, the client device 104, and/or theclient device 106 using various protocols such as Transmission ControlProtocol/Internet Protocol (TCP/IP), among other possibilities.

In some embodiments, the network server device 102 may be configured toreceive account data, e.g., data/data packets 122 and/or 124, from anumber of client devices 104 and/or 106, where the client devices 104and/or 106 correspond to a number of account profiles. The accountengine 112 may segment the number of account profiles into a number ofprofile groups based on a respective balance associated with eachaccount profile. The account engine 112 may also determine a number oftarget accounts from the number of profile groups based on behavioraldata retrieved from the account data 122 and/or 124. The data processingcomponents 114 and/or 116 may determine a method of contact for eachtarget account based on the behavioral data. The data processingcomponents 114 and/or 116 may also determine a respective time tocommunicate with a respective device for each target account based onthe methods of contact. As such, the communication component 118 mayinitiate communications to the respective devices, e.g., client devices104 and/or 106, at the respective times for each target account.

It can be appreciated that the network server device 102 and the clientdevices 104 and 106 illustrated in FIG. 1 may be deployed in other ways.The operations performed and/or the services provided by such clientdevices 104 and 106 may be combined or separated for a given embodimentand may be performed by a greater number or fewer number of devices.Further, one or more devices may be operated and/or maintained by thesame or different entities.

In some embodiments, an account, possibly also referred to simply as auser account, may be a compilation of data associated with a given user.As such, some examples of accounts may include accounts with service orpayment providers such as PayPal, Inc. of San Jose, Calif., USA and/orother types of financial, transactional, and/or e-commerce relatedaccounts. Further, accounts may also include social networking accounts,e-mail accounts, smartphone accounts, music playlist accounts, videostreaming accounts, among other possibilities. For example, an accountfor a particular user may include data related to the user, data relatedto the user's interest, and/or data representing the user. Further, theuser may provide various types of data to a user device, e.g., clientdevices 104 and/or 106, with access to the account.

The user account may be displayed on a client device, possibly throughI/O interfaces such as those described above in relation to FIG. 1.Thus, the user account may be displayed on a smartphone, a laptopcomputer, and/or a wearable computing device that may be used to accessthe account. The user may operate the client or computing device andtheir account may be managed on the computing device. For example, acomputing device may be used to view and/or send one or more requests,user requests and/or related data, account data, purchase ortransactional data, item or item description data, user device data,electronic messages and/or related data, requirement data,authentication data, biometric data, user data received in response toelectronic messages, and/or other types of data described above.

In some embodiments, a user may have a single account such as an accountwith a service or payment provider representing the user for multipleother accounts described above such as e-mail accounts, socialnetworking accounts, smartphone accounts, as well as websites,applications, and/or other services. For example, a user could opt touse their account as a multi-purpose account for performing variousoperations, including initiating user requests to purchase one or moreitems and authenticating or verifying the user requests.

In some embodiments, a user account may be created by one or more users.For example, one account may be a family account where a number offamily members or users may have access to the family account. In someinstances, the account may be a corporate account, where employees,staff, worker personnel, and/or contractors, among other individuals mayhave access to the corporate account. Yet further, it should be notedthat a user, as described herein, may be a robot, a robotic system, acomputing device, a computing system, and/or another form of technologycapable of sending and receiving data corresponding to the account. Auser may be required to provide a login, a password, a code, anencryption key, authentication data, biometric data, and/or other typesof data to gain access to the account.

FIG. 2A illustrates an exemplary network server device 200 configured tosupport a set of trays, according to an embodiment. The network serverdevice 200 may, for example, take the form of the network server device102 described above in relation to FIG. 1. Further, the network serverdevice 200 may be configured to support, operate, run, and/or managenumerous user accounts or profiles, and various types of data includingone or more requests, user requests and/or related data, user accountdata, purchase or transactional data, and/or other types of datadescribed above.

As shown, network server device 200 may include a chassis 202 that maysupport trays 204 and 206, and possibly multiple other trays as well.The chassis 202 may include slots 208 and 210, among other possibleslots, configured to hold trays 204 and 206, respectively. For example,the tray 204 may be inserted into the slot 208 and the tray 206 may beinserted into the slot 210. Yet, the slots 208 and 210 may be configuredto hold the trays 204 and 206 interchangeably such that the slot 208 maybe configured to hold the tray 206 and the slot 210 may be configured tohold the tray 204. For example, during operation of server device 200,the tray 204 may be inserted into the slot 208 and the tray 206 may beinserted into the slot 210. Further, the trays 204 and 206 may beremoved from the slots 208 and 210, respectively. Yet further, the tray204 may be inserted into the slot 210 and the tray 206 may be insertedinto the slot 208, and the network server device 200 may continueoperating without interruptions.

The chassis 202 may be connected to a power supply 212 via connections214 and 216 to supply power to the slots 208 and 210, respectively. Thechassis 202 may also be connected to the communication network 218 viaconnections 220 and 222 to provide network connectivity to the slots 208and 210, respectively. As such, trays 204 and 206 may be inserted intoslots 208 and 210, respectively, and power supply 212 may supply powerto trays 204 and 206 via connections 214 and 216, respectively. Further,trays 204 and 206 may be inserted into slots 210 and 208, respectively,and power supply 212 may supply power to trays 204 and 206 viaconnections 216 and 214, respectively. Yet further, trays 204 and 206may be inserted into slots 208 and 210, respectively, and communicationnetwork 218 may provide network connectivity to trays 204 and 206 viaconnections 220 and 222, respectively. In addition, trays 204 and 206may be inserted into slots 210 and 208, respectively, and communicationnetwork 218 may provide network connectivity to trays 204 and 206 viaconnections 222 and 220, respectively.

The communication network 218 may, for example, take the form ofcommunication network 108 described above in relation to FIG. 1,possibly including one or both of the data network 108A and the cellularnetwork 108B. In some embodiments, the communication network 218 mayprovide a network port, a network hub, a network switch, or a networkrouter that may be connected to an Ethernet link, an opticalcommunication link, a telephone link, among other possibilities.

FIG. 2B illustrates an exemplary tray 204 configured to support one ormore components, according to an embodiment. The tray 204 may, forexample, take the form of tray 204 described in relation to FIG. 2A.Further, the tray 206 may also take the form of the tray 204. As shown,the tray 204 may include a tray base 230 as the bottom surface of thetray 204 configured to support multiple components such as a maincomputing board connecting one or more components 230-238. The tray 204may include a connection 226 that may link to the connections 214 or 216to supply power to the tray 204. The tray 204 may also include aconnection 228 that may link to the connections 220 or 222 to providenetwork connectivity to the tray 204. The connections 226 and 228 may bepositioned on the tray 204 such that upon inserting the tray 204 intothe slot 208, the connections 226 and 228 couple directly with theconnections 214 and 220, respectively. Further, upon inserting the tray204 into the slot 210, the connections 226 and 228 may couple directlywith connections 216 and 222, respectively.

The tray 204 may include components 232-240. In particular, the tray 204may include the account engine 232, the contact data processingcomponent 234, the time data processing component 236, the communicationcomponent and/or interface 118, and the memory component 240 that may,for example, take the form of the account engine 112, the contact dataprocessing component 114, the time data processing component 116, thecommunication component and/or interface 118, and the memory component120. As such, the connections 226 and 228 may be configured to providepower and network connectivity, respectively, to each of the components232-240. In some embodiments, one or more of the components 232-240 maybe configured or supported via one or more circuits that includeresistors, inductors, capacitors, voltage sources, current sources,switches, logic gates, registers, and/or a variety of other circuitcomponents to perform operations described herein and illustrated by theaccompanying figures. In some embodiments, the network server device 200may execute instructions on a non-transitory, computer-readable mediumto configure or support one or more of the components 232-240 to performsuch operations.

As shown, the account engine 232, possibly also referred to as anaccount management engine, may include numerous databases for storing,processing, and/or securing user account data in the network serverdevice 200. For example, the account management engine may includerelational databases, possibly to perform account data processing and/oronline analytical processing of user account data. The accountmanagement engine may generate numerous search queries, search multipledatabases in parallel, and produce search results simultaneously and/orconsecutively. As such, the account engine 232 may create multipletables, database objects, indices, and/or views to perform accountmanagement and/or analytical processing of numerous accounts and/oraccount profiles.

Any two or more of the components 232-240 described above may becombined to take the form of one or more multi-purpose processors,microprocessors, and/or special purpose processors, among other types ofprocessors. For example, two or more of the account engine 232, thecontact data processing component 234, the time data processingcomponent 236, the communication component and/or interface 118, and thememory component 240 may be combined. Further, the combined device maytake the form of one or more network processors, DSPs, PSOCs, FPGAs,and/or ASICs, among other types of processing devices and/or components.As such, the combined device may be configured to carry out variousoperations of the components 232-240.

It should be noted that components 232-240 described above may providebenefits, improvements, and/or advantages over conventional orgeneral-purpose servers and/or computers. For example, components232-240 may segment account profiles, determine target accounts, and/ordetermine methods of contacts and/or respective times to communicatewith respective devices more efficiently through the machine learningconfigurations of components 232-240 as described herein. For example,configuring separate and dedicated processing components 114 and/or 116for determining methods of contact and respective times to communicatewith respective device may optimize performance beyond the capabilitiesof conventional servers and/or general-purpose computers.

In some embodiments, the network server device 200 may be configured toreceive account data, e.g., data/data packets 122 and/or 124, from anumber of client devices 104 and/or 106, where the client devices 104and/or 106 correspond to a number of account profiles. The accountengine 232 may segment the number of account profiles into a number ofprofile groups based on a respective balance associated with eachaccount profile. The account engine 232 may also determine a number oftarget accounts from the number of profile groups based on behavioraldata retrieved from the account data 122 and/or 124. The data processingcomponents 234 and/or 236 may determine a method of contact for eachtarget account based on the behavioral data. The data processingcomponents 234 and/or 236 may also determine a respective time tocommunicate with a respective device, e.g., client devices 104 and/or106, for each target account based on the methods of contact. As such,the communication component 118 may initiate communications to therespective devices, e.g., client devices 104 and/or 106, at therespective times for each target account.

It can be appreciated that the network server device 200, the chassis202, the trays 204 and 206, the slots 208 and 210, the power supply 212,the communication network 218, and the components 232-240 may bedeployed in other ways. The operations performed by components 232-240may be combined or separated for a given embodiment and may be performedby a greater number or fewer number of components or devices. Further,one or more components or devices may be operated and/or maintained bythe same or different entities.

FIG. 3A illustrates an example system 300A and a number of clientdevices, according to an embodiment. As shown, the client device 302 maytake the form of a tablet device or a point-of-sale (POS) deviceconfigured to access merchant accounts or account profiles 310 and 314with a service provider, such as those described above. The clientdevice 304 may take the form of a (first) smartphone configured toaccess accounts or account profiles 318 and 322 with the serviceprovider. The client device 306 may take the form of a laptop computerconfigured to access seller accounts or account profiles 326, 330, and334 with the service provider. The client device 308 may take the formof a (second) smartphone 308 configured to access user account oraccount profile 338 with the service provider. Notably, each of theclient devices 302-308 may have access to more than one account.Further, each of client devices 302-308 may receive usernames andpasswords to enable access to respective accounts 310, 314, 318, 322,326, 330, 334, and/or 338.

In some embodiments, the system 300A may take the form of the one ormore systems described above to exchange data or data packets 300 over acommunications network and to perform machine learning operations. Thesystem 300A may include a network server device (e.g., the networkserver device 102 and/or 200) that receives account data or data packets300, from the number of client devices 302-308, where the number ofclient devices 302-308 corresponds to a number of account profiles 310,314, 318, 322, 326, 330, 334, and/or 338. It should be noted that theaccount profiles may be referred to and/or represented as data packets310, 314, 318, 322, 326, 330, 334, and/or 338. For example, these datapackets may take the form of data packets 122 and/or 124.

An account engine of the network server device such as those describedabove may segment the number of account profiles 310, 314, 318, 322,326, 330, 334, and/or 338 into a number of profile groups 342, 344,and/or 346 based on a respective balance associated with each accountprofile. For example, the account engine may segment account profiles314, 326, and/or 330 into profile group 342 based on low balancesassociated with each of the account profiles 314, 326, and/or 330. Theaccount engine may segment account profiles 318 and/or 334 into profilegroup 344 based on zero or near zero balances associated with each ofthe account profiles 318 and/or 334. The account engine may segmentaccount profiles 310, 322, and/or 338 into profile group 346 based onnegative balances associated with each of the account profiles 310, 322,and/or 338. It should be noted that the account engine may segment theaccount profiles 310, 314, 318, 322, 326, 330, 334, and/or 338 invarious ways based on the account profiles, the account data, and/orother machine learning configurations of the network server device.

The account engine may determine a number of target accounts 310, 318,and/or 338 from the number of profile groups 344 and 346 based onbehavioral data 312, 320, and/or 340 retrieved from the account data300. The one or more data processing components of the network serverdevice determines a method of contact for each target account 310, 318,and/or 338 based on the behavioral data 312, 320, and/or 340,respectively. The one or more data processing components of the networkserver device further determines a respective time to communicate with arespective device, e.g., client devices 302, 304, and 308, for eachtarget account 310, 318, and/or 338, respectively, based on the methodsof contact. In addition, the one or more communication components of thenetwork server device may initiate communications to the respectivedevices, e.g., client devices 302, 304, and 308, at the respective timesfor each target account 310, 318, and/or 338, respectively. It should benoted that the one or more data processing components may be configuredto perform machine learning operations to increase the probability ofreaching targeted users with the communications initiated.

FIG. 3B illustrates data packets 300B and a number of target accounts310, 318 and/or 338, according to an embodiment. It should be noted thatthese target accounts 310, 318, and/or 338 may take the form of datapackets to be referred to as data packets 310, 318, and/or 338,respectfully. As shown, target accounts 310, 318, and/or 338 may includebehavioral data 312, 320, and/or 340, respectively. Behavioral data 312may include contact data 352, time data 354, device data 356, locationdata 358, payment data 360, pattern data 362, and/or peer data 364.Behavioral data 320 may include contact data 366, time data 368, devicedata 370, and/or payment data 372. Behavioral data 340 may includecontact data 374, time data 376, device data 378, location data 380,payment data 382, pattern data 384, recurring payment data 386, and/orpeer data 388.

The contact data 352, 366, and/or 374 may include phone number data,email address data, electronic messaging data, mailing address data, faxnumber data, application programming interface (API) data, and/or otherforms of data related to contacting or communicating with the clientdevice 302, 304, and/or 308. The time data 354, 368, and/or 376 mayinclude calendar data from one or more digital calendars, schedule dataincluding event or status data, alarm data indicative of one or moreparticular times the alarms are set to go off or sound, and/or otherforms of data possibly stored in the respective client devices 302, 304,and/or 308, and related to time. For example, the network server devicesmay process alarm data to perform machine learning and thereby sendnotifications to targeted users after alarms are set off on the clientdevices 302, 304, and/or 308. As such, the targeted users may becontacted just after waking up and turning off alarms on the respectiveclient devices 302, 304, and/or 308.

The device data 356, 370, and/or 378 may include data indicative of anoperating mode of the client device 302, 304, and/or 308 (e.g., whetherthe device is on, off, in standby mode, sleep mode, or a busy mode). Thedevice data 356, 370, and/or 378 may also include protocol dataprocessed by the server data such as cellular protocol data, includingGSM, CDMA, UMTS, EV-DO, WiMAX, or LTE data, radio-frequency identifier(RFID) data, such as near-field communications (NFC) data, among otherpossibilities. The device data 356, 370, and/or 378 may be processed todetermine when targeted users may be unavailable. For example, thenetwork server devices may process the operating mode data to determinewhen the client devices 302, 304, and/or 308 may be in standby mode,sleep mode, and/or busy mode. As such, the network server devices mayavoid attempting communications with the client devices 302, 304, and/or308 at such times.

Location data 358 and/or 380 may include geo location data, WiFibeaconing data, SSID reading data, Bluetooth data, and/or othernear-field communication data. Further, location data 358 and/or 380 mayinclude Enhanced Observed Time Difference (EOTD) data, Assisted GPS(A-GPS) data, Differential GPS (DGPS) data, Time Difference of Arrival(TDOA) data, Angle of Arrival data, triangulation data, localtransceiver pilot signal data, among other forms of location datadescribed above. Location data 358 and/or 380 may also be processed todetermine when to attempt communications with client devices 302, 304,and/or 308. For example, in instances where location data 358 and/or 380indicate work locations of the targeted users, the network serverdevices may avoid attempting communications with the client devices 302and/or 308 at such times. For instance, the network server devices maydelay or time the communications to times when the client devices 302and/or 308 have left the work locations to better approximate reachingthe targeted users. It should be noted that client device 304 may haveits location setting turned off such that the network server device maynot be able to retrieve location data indicating the location of theclient device 304. As such, the network server may rely on other typesof data 366, 368 370, and/or 372 to initiate communications to theclient device 304.

Payment data 360, 372, and/or 382 may include balance data, availablebalance data, present balance data, total balance data, activity data,statement data, and/or transfer data, among other forms of payment ortransactional data related to the target accounts 310, 318, and/or 338.The pattern data 362 and/or 384 may include data regarding whentransactions were completed, attempted, and/or denied, among other formsof data indicative when payments occur for the target accounts 310and/or 338. The recurring payment data 386 may include data associatedwith automated payment data, payment plan data, payment date data,and/or other forms of data indicative of scheduled payments. Peer data364 and/or 388 may include data regarding other accounts or targetaccounts such as account 314, 322, 326, 330, and/or 334.

In some embodiments, each of target accounts 310, 318 and/or 338indicates a low balance, a zero balance, a negative balance, and/or anoverdue balance. For example, target account 318 may have a low accountbalance and target accounts 310 and/or 338 may have negative accountbalances. Further, the behavioral data 312, 320, and/or 340 for each oftarget accounts 310, 318 and/or 338 may indicate a number of latepayments, missed payments, and/or rejected payments made for each targetaccount. For example, behavioral data 312 may include payment data 360that indicates a number of late payment made for the target account 310.The behavioral data 320 may include payment data 372 that indicates anumber of missed payments and/or rejected payments made under the targetaccount 318. Behavioral data 340 may include payment data 382 thatindicates a number of late payments, missed payments, and/or rejectedpayments made for the target account 338.

In some embodiments, the behavioral data 312 and/or 340 may includelocation data 358 and/or 380, respectively, indicative of locations ofthe respective devices 302 and/or 308. The location data 358 and/or 380may include GPS data, beacon data, WI-FI data, base station data,triangulation data, sensor data, movement data (e.g., accelerationand/or velocity data), temperature data, and/or other types of datadescribed above. In some instances, the location data 358 and/or 380 mayinclude sensor data retrieved by the number of client devices 302-308collectively, where the sensor data may indicates the locations of therespective devices 302, 304, and/or 308 identified from the number ofclient devices 302-308.

In some embodiments, the behavioral data 312, 320, and/or 340 mayinclude payment data 360, 372, and/or 382, respectively indicative oftimes of payments made or missed for at least one of the target accounts310, 318, and/or 338. Further, the behavioral data 312 and/or 340 mayinclude pattern data 362 and/or 384 indicative of patterns of paymentsmade or missed for at least one of the target accounts 310 and/or 338.Further, the behavioral data 340 may include recurring payment data 386indicative of one or more recurring payments or automated payments forat least one of the target accounts 338.

In some embodiments, the behavioral data 312 and/or 340 further includepeer data 364 and/or 388, respectively, and/or social media dataincluded in the peer data 364 and/or 388 that may be indicative ofmethods to contact peer accounts associated with the number of targetaccounts 310, 318, and/or 388. The one or more data processingcomponents may determine the method of contact for each target account310, 318, and/or 338 based on the methods to contact peer accounts, suchas accounts 314, 322, 326, 330, and/or 334. For example, peer data 364may be indicative of methods, e.g., cell phone or email, to contact apeer account 326 associated with the target account 310. In particular,the peer account 326 and the target account 310 may have interacted formaking or receiving payments and/or transactions, among other types ofaccount activities. As such, the one or more data processing componentsmay determine the method of contact for target account 310 to be viacell phone or email.

In some embodiments, the behavioral data 312, 320, and/or 340 associatedwith the number of target accounts 310, 318, and/or 338 indicates timesassociated with deposits submitted to each of the target accounts 310,318, and/or 338. As such, the one or more data processing components maydetermine the respective times to communicate with the respectivedevices 302, 304, and/or 308 for each the target account 310, 318,and/or 338, respectively, based on the times associated with thedeposits submitted. By communicating with the respective devices 302,304, and/or 308 shortly after deposits are submitted to thecorresponding target accounts 310, 318, and/or 338, the probability ofthe targeted account balance being paid off may be increased.

FIG. 3C is a flowchart of an exemplary method 390, according to anembodiment.

Notably, one or more steps of the method 390 or other methods/processesdescribed herein may be omitted, performed in a different sequence,and/or combined for various types of applications. At step 391, themethod 390 may include determining a probability score for each of thetarget accounts 310, 318, and/or 338 based on a probability that therespective target account receives a payment. For example, theprobability score may be a score from 1 to 999 based on the probabilitythat the respective target account receives a payment to pay off anegative balance of the target account, where 0 is the lowestprobability and 999 is the highest probability. For instance, aprobability score of 300 or 400 may be determined for the target account310 based payment data 360 indicating payments historically received topay off negative balances of the target account previously incurred. Inanother example, the probability score may be a score from 1 to 5 basedon the probability that the respective target account receives a paymentto pay off a negative balance of the target account, where 0 is thelowest probability and 5 is the highest probability.

At step 392, the method 390 may include determining a method score foreach of the target accounts 310, 318, and/or 338 based on the determinedmethod of contact for each of the target accounts 310, 318, and/or 338.For example, the method score may be a score from 1 to 5 based on thedetermined method of contact including, where 0 is the lowest methodscore and 5 is the highest method score. For instance, a method score of3 or 4 may be determined for the target account 310 based on a cellphone number identified of client device 302. Yet, a method score of 1or 2 may be determined based on an email address identified associatedwith the target account 310. In particular, the method score may behigher or increased based on a higher probability of contacting thetargeted user using the method of contact determined.

At step 393, the method 390 may include determining a time score foreach of the target accounts 310, 318, and/or 338 based on the respectivetimes determined to communicate with the respective devices 302, 304,and/or 308. For example, the time score may be a score from 1 to 5 basedon the determined time to communicate with the respective devices 302,304, and/or 308, where 0 is the lowest time score and 5 is the highesttime score. For instance, a method score of 4 may be determined for thetarget account 310 based on five to six different time periodsdetermined to communicate with the client device 302 to reach thetargeted user, possibly with a determined probability of reaching thetargeted user. As such, the time score may be higher based on the numberof times determined to communicate with the client device 302.

At step 394, the method 390 may include initiating the communicationsbased on the probability score, the method score, and/or the time score.For example, the probability score, the method score, and/or the timescore may be combined, averaged, and/or summed to determine an overallscore. For instance, the overall score for target account 310 may behigher than that of target accounts 318 and/or 338. As such, thecommunications to client device 302 may be prioritized over thecommunications to client devices 304 and/or 308. It should be noted thatthe more data retrieved by the network server device associated with thetarget account 310 may lead to the overall score of the target account310 being higher than the target account 318. Yet, in some instances,the network server device may determine a higher probability score forthe target account 310 than that of the target account 338.

In some embodiments, the one or more communication components mayinitiate the communications with at least one of an email communication,a text or SMS communication, and/or a telephonic communication to therespective devices 302, 304, and/or 308 at the respective times based onthe probability score, the method score, the time score, and/or theoverall score as described above.

In some embodiments, a non-transitory computer-readable medium may havestored thereon instructions. The instructions, when executed by a serverdevice (e.g., the network server devices 102 and/or 200) cause theserver device to perform operations, such as the machine learningoperations described herein. The operations may include accessing, bythe server device, account data 300 from a number of user devices 304and/or 308, and merchant devices 302 and/or 306 that correspond to anumber of accounts 310, 314, 318, 322, 326, 330, 334, and/or 338. Theoperations may also include accessing, by an account engine of theserver device, a profile for each account of the number of accounts 310,314, 318, 322, 326, 330, 334, and/or 338, based on a respective numberof delinquent actions associated with each account 310, 314, 318, 322,326, 330, 334, and/or 338. For example, the respective number ofdelinquent actions associated with each account 310, 314, 318, 322, 326,330, 334, and/or 338 may indicate a number of missed payments associatedwith each account, a number late payments associated with each account,and/or a number of rejected payments associated with each account, amongother types of account activities. For example, other types ofactivities may include transfer of invalid instruments, goods, and/orservices, where the targeted user owes another user funds or property.

In some instances, the operations may include determining a profile foreach account of the number of accounts 310, 314, 318, 322, 326, 330,334, and/or 338, where each of the profiles may be different, similar,or the same. For example, the profiles for accounts 314, 326, and/or 330in group 342 may be the same profile, such as a “missed payments”profile. The profiles for accounts 318 and/or 334 in group 344 may bethe same profile, such as a “late payments” profile. The profiles foraccounts 310, 322, and/or 338 in group 346 may be the same profile, suchas a “rejected payments” profile.

The operations may also include determining, by the account enginedescribed herein, a number of target accounts 310, 318, and/or 338 fromthe number of accounts 310, 314, 318, 322, 326, 330, 334, and/or 338based on the profiles of each of the accounts 310, 314, 318, 322, 326,330, 334, and/or 338. In some embodiments, determining the number oftarget accounts 310, 318, and/or 338 is further based on respectivebalances of each account of the number of accounts 310, 314, 318, 322,326, 330, 334, and/or 338.

The operations may also include determining, by a data processingcomponent of the server device described herein, a method of contact foreach of the target accounts 310, 318, and/or 338 based on behavioraldata 312, 320, and/or 340 retrieved from the account data 300. Theoperations may also include determining, by the data processingcomponent, a respective time to communicate with the respective devices302, 304, and/or 308 for each of the target accounts 310, 318, and/or338 based on the methods of contact. The operations may also includeinitiating, by a communication component of the server device,communications to the respective devices 302, 304, and/or 308 at therespective times for each of the target accounts 310, 318, and/or 338.

In some embodiments, referring back to FIG. 3B, the behavioral data 312and/or 340 further includes peer data 364 and/or 388, respectively,and/or social media data in the peer data 364 and/or 388 that may beindicative of methods to contact peer accounts associated with thenumber of target accounts 310, 318, and/or 338. Further, the operationsmay include determining the method of contact for each of the targetaccounts 310, 318, and/or 338 based on the methods to contact peeraccounts. For example, peer data 364 may be indicative of a method tocontact, e.g., via cell phone or email, a peer account 326 associatedwith the target account 310. As such, the one or more data processingcomponents may determine the method of contact for target account 310 tobe via cell phone or email.

In some embodiments, the behavioral data 312, 320, and/or 340 associatedwith the number of target accounts 310, 318, and/or 338 may indicatetimes associated with deposits submitted to each of the target accounts310, 318, and/or 338. Further, determining the respective times tocommunicate with the respective devices 302, 304, and/or 308 for each ofthe target accounts 310, 318, and/or 338 may be based on the timesassociated with the deposits submitted. As noted, by communicating withthe respective devices 302, 304, and/or 308 shortly after deposits aresubmitted to the corresponding target accounts 310, 318, and/or 338, theprobability of the balances of the targeted accounts 310, 318, and/or338 being paid off partially or fully may be increased.

FIG. 3D illustrates data packets 300D and score data of a number of thetarget accounts 310, 318 and/or 338, according to an embodiment. Itshould be noted that these target accounts 310, 318, and/or 338 may takethe form of data packets to be referred to as data packets 310, 318,and/or 338, respectfully. As shown, target account 310 may includeprobability score data 310P, method score data 310M, and time score data310T. It should be noted that the data 310P, 310M, and/or 310T may takethe form of data packets to be referred to as data packets 310P, 310M,and/or 310T, respectfully. The probability score data 310P may berepresentative of the payment data 360, pattern data 362, and/or peerdata 364. For example, probability score data 310P may indicate theprobability score of the target account 310 based on respective weightsor values applied to the payment data 360, pattern data 362, and/or peerdata 364. The method score data 310M may be representative of contactdata 352, device data 356, and/or location data 358. For example, methodscore data 310M may indicate the method score of the target account 310based on respective weights or values applied to the contact data 352,device data 356, and/or location data 358. The time score data 310T maybe representative of time data 354. The time score data 310T mayindicate the time score of the target account 310 based on the time data354.

The target account 318 may include the probability score data 318P,method score data 318M, and time score data 318T. It should be notedthat the data 318P, 318M, and/or 318T may take the form of data packetsto be referred to as data packets 318P, 318M, and/or 318T, respectfully.The probability score data 318P may indicate the probability score basedon the payment data 372. The probability score data 318P may indicatethe probability score for the target account 338 based on the paymentdata 372. The method score data 318M may be representative of contactdata 366 and/or device data 370. The method score data 318M may indicatethe method score of the target account 318 based on respective weightsor values applied to the contact data 366 and/or device data 370. Thetime score data 318M may include or be representative of time data 368.The time score data 318T may indicate the time score of the targetaccount 318 based on the time data 368.

The target account 338 may include probability score data 338P, methodscore data 338M, and time score data 338T. It should be noted that thedata 338P, 338M, and/or 338T may take the form of data packets to bereferred to as data packets 338P, 338M, and/or 338T, respectfully.Probability score data 338P may include or be representative of therecurring payment data 386, payment data 382, pattern data 384, and/orpeer data 338. For example, probability score data 338P may indicate theprobability score of the target account 338 based on respective weightsor values applied to the recurring payment data 386, payment data 382,pattern data 384, and/or peer data 388. Method score data 338M may berepresentative of contact data 374, device data 378, and/or locationdata 380. For example, method score data 338M may indicate the methodscore of the target account 338 based on respective weights or valuesapplied to the contact data 374, device data 378, and/or location data380. Time score data 338T may be representative of time data 376. Thetime score data 338T may indicate the time score of the target account338 based on the time data 376.

In some embodiments, the instructions, when executed by a server device(e.g., the network server devices 102 and/or 200) cause the serverdevice to perform operations, such as the machine learning operationsdescribed herein. The operations performed by the server device mayinclude determining or accessing a probability score for each of thetarget accounts 310, 318, and/or 338 based on a probability that therespective target account 310, 318, and/or 338 receives a payment.

In practice, the probability score of the target account 310 may beincreased based on the weight or value applied to the pattern data 362,possibly where the weight or value exceeds that of the payment data 360and/or peer data 364. For example, the pattern data 362 may indicatetimes associated with deposits submitted to the target account 310,possibly to pay off a negative balance of the target account 310. Forinstance, the times associated with the deposits submitted may be justafter the first day of every month, possibly when the targeted usersubmits deposits to the target account 310 after receiving a paymentamount from an employer. Thus, the pattern data 362 indicative of thepattern of deposits submitted may increase the probability score of thetarget account 310. In another example, the probability score of thetarget account 338 may be increased based on the weight or value appliedto the recurring payment data 386, possibly where the weight or valueexceeds that of the payment data 382, pattern data 384, and/or peer data388. For example, recurring payment data 362 may indicate prior paymentsettings of a separate account that the target account 338 debits on amonthly basis to pay off a negative balance of the target account 338.In some instances, the prior payment settings may be turned off orinactive. Thus, the automated payment settings indicated by therecurring payment data 362 may increase the probability score of thetarget account 338 such that the targeted user may be contacted to turnon the prior payment settings or activated the settings to continue withthe monthly payments to pay off the negative balances.

Further, the operations may include determining or accessing a methodscore for each of the target accounts 310, 318, and/or 338 based on thedetermined method of contact for each of the target accounts 310, 318,and/or 338. In practice, the method score of the target account 310 maybe increased based on the weight or value applied to the location data358, possibly where the weight or value exceeds that of the contact data352 and/or device data 356. For example, the location data 358 mayprovide an indication of the targeted user's availability, possiblywhere the targeted user may be located outdoors or in transit such thatthe targeted user may be contacted via the client device 302. Thus, thelocation data 358 indicative of the location of the client device 302may increase the method score of the target account 310. In anotherexample, the method score of the target account 318 may be modifiedbased on the weight or value applied to the device data 370, possiblywhere the weight or value exceeds that of the contact data 366. Forexample, the device data 370 may indicate that the client device 304 isturned off, low in battery power, in sleep mode, in hibernation mode,possibly indicating that the method score related to contacting thetargeted user via the client device 304 is lower than normal or otherinstances.

Further, the operations may include determining or accessing a timescore for each of the target accounts 310, 318, and/or 338 based on therespective times determined to communicate with the respective devices302, 304, and/or 308. For example, the time score of the target account310 may be higher (e.g., 4 or 5 in the scale from 1 to 5) based on thetime data 354 (e.g., the targeted user's schedule or calendar data)indicating that the targeted user is on lunch break from work. Inanother example, the time score of the target account 338 may be lower(e.g., 1 or 2 in the scale from 1 to 5) based on the time data 368(e.g., the targeted user's schedule or calendar data) indicating thatthe targeted user is at the gym, possibly where the targeted user may beunavailable or away from their client device 308.

Thus, the operations may include initiating the communications to therespective devices 302, 304, and/or 308 based on the respectiveprobability score, the method score, and the time score for each of thetarget accounts 310, 318, and/or 338. For example, the communicationinitiated to client device 302 for the target account 310 may be a phonecommunication such as an automated voice communication based on thelocation data 358 and/or the time data 354 described herein. Further,the communication initiated to the client device 304 for the targetaccount 318 may be a text or SMS communication based on the device data370 and/or the time data 368 described herein. In addition, thecommunication initiated for the target account 338 may be an emailcommunication or notification regarding the automated payments to debitthe target user's checking account or other account possibly alsomaintained by the server device, in accordance with the recurringpayment data 362 described above. Thus, initiating the communicationsmay include initiating an email communication, a text or SMScommunication, and/or a phone communication to the respective devices302, 304, and/or 308 at the respective times based on the probabilityscores, the method scores, and the time scores respective to the targetaccounts 310, 318, and/or 338, as described herein.

FIG. 3E illustrates data packets 300E and ranking a number of the targetaccounts 310, 318 and/or 338, according to an embodiment. It should benoted that these target accounts 310, 318, and/or 338 may take the formof data packets to be referred to as data packets 310, 318, and/or 338,respectfully. In some embodiments, the instructions described above,when executed by a server device (e.g., the network server devices 102and/or 200) may cause the server device to perform operations, such asthe machine learning operations described herein. The operations mayinclude ranking the number of target accounts 310, 318 and/or 338, basedon the account profile for each account of the number of accounts 310,318, and/or 338. For example, target account 310, possibly also referredto as the account profile 310, may indicate “slacker.” The targetaccount 318, possibly also referred to as the account profile 318, mayindicate “unreliable” and/or “unpredictable.” Yet further, the targetaccount 338, possibly also referred to as the account profile 318, mayindicate “troubled,” and/or “fraudster.” Thus, the target account 310may be ranked higher than the target account 318, and further, thetarget account 318 may be ranked higher than the target account 338. Assuch, the server device may attempt to contact the target account 310 ata first time, then the target account 318 at a second time after thefirst time, and then the target account 338 at a third time after thesecond time. Thus, initiating the communications to the respectivedevices 302, 304, and/or 308 may be based at the ranking of the numberof target accounts 310, 318, and/or 338, respectively.

In some embodiments, the ranking data 310R, 318R, and 338R may eachindicate a value or score such that each of the corresponding targetaccounts 310, 318, and/or 338, respectively, may be ranked against eachother. Thus, initiating the communications to the respective devices302, 304, and/or 308 may be based at the ranking of the number of targetaccounts 310, 318, and/or 338, respectively. For example, the machinelearning operations may include processing the account data 310, 318,and/or 338 to determine the ranking of each of the target accounts 310,318, and 338. In particular, the ranking data 310R may indicate that thetarget account 310 is ranked first based on the server device processingthe location data 358 and/or time data 354 indicative of the targeteduser carrying the client device 302 from an office location to a foodcourt location, possibly while the targeted user is on a lunch break.The ranking data 318R may indicate that the target account 318 is rankedsecond based on the device data 370 indicating that the client device304 accepts a phone call, thereby designating a busy wireless signalreceived by the server device over the network. The ranking data 338Rmay indicate that the target account 338 is ranked third based on theserver device processing the time data 376 and/or location data 380indicating that the targeted user is at the gym.

FIG. 4 illustrates an exemplary input/output (I/O) interface 404 of aclient device 400, according to an embodiment. As shown, the clientdevice 400 may take the form of one of the client devices 302, 304, 306,and/or 308 described above in relation to FIGS. 3A-3E. The I/O interface404 may take the form of I/O interfaces 130 and/or 140 as describedabove. As shown, the client device 400 may display a time 402 via theI/O interface 404.

In some embodiments, a non-transitory computer-readable medium may havestored thereon instructions that, when executed by the client device400, cause the client device 400 to perform operations. In someinstances, the operations may include displaying, by the I/O interface404 of the client device 400, account or account data 406 that mayinclude profile data indicative of the account profile accessible by theclient device 400. The operations may also include displaying, by theI/O interface 404, communication data 408 possibly received from aserver device described herein. As shown, communication data 408 mayinclude text indicating, “Your Account Balance is Negative.” Forexample, communication data 408 that may take the form of emailcommunication data, a text or SMS communication data, and/or atelephonic communication data, possibly displaying words throughvoice-recognition processing.

Further, the operations may also include displaying a balance data input410 where upon selection (e.g., a touch selection input) of the balancedata input 410, the one or more respective balances of the account 406may be displayed by the I/O interface 404. Yet further, communicationdata 408 may include an option 412 to make a payment. Based on selectingthe yes or no inputs 414, the client device 400 may be operated to makea payment to the one or more respective balances of the account 406. Insome instances, the client device 400 may receive authentication orsensor data 416 via sensor 418 to make the payment to the one or morerespective balances of the account 406. In some instances, the yes input414 and the authentication or sensor data 416 may be enteredsimultaneously by a touch-selection of the yes input 414 and afingerprint scan via the sensor 418. In some instances, making thepayment may require such simultaneous actions to ensure properauthenticity in making the payment.

FIG. 5 is an exemplary system 500, according to an embodiment. In someinstances, a server device such as those described above may performmachine learning operations to process data accordingly and/or sendcommunications to client devices. FIG. 5 is an example of the serverdevice processing behavioral data including location data describedabove and/or co-location data. In particular, the server device maydetermine times to communicate with a client device based on predictingwhen the targeted user will attempt to make a payment under theirtargeted account. As such, the server device may perform machinelearning operations, thereby initiating communications to client devicesassociated with target accounts accordingly. As shown, the clientdevices 502 and/or 504 may take the form of any of the client devices302, 304, 306, 308, and/or 400 described above, among other clientdevices in relation to FIGS. 1-4. Further, the input/output (I/O)interfaces 506 and/or 508 may take the form of any of the I/O interfaces130, 140, and/or 404.

As illustrated, the account 510 accessed by the client device 502 may bea target account. For example, the target account 510 may be determinedby the server device based on identifying that the balance of the targetaccount 510 is low, zero, or negative. In some instances, the serverdevice may determine a time to communicate with client device 502 basedon behavioral data indicating a threshold radius 534 around the location522 of the client device 502 and/or an area 536 of the client device 502derived from the threshold radius 534. For example, consider a scenariowhere the client device 502 enters the merchant store 530, possiblyproximate to the other stores 526, 528, and/or 532 shown on the map 518for illustrative purposes. In particular, the movement of the clientdevice 502 may cause the merchant store 530 to be within the area 536 ofthe client device 502. As such, the server device may process thebehavioral data including location data indicative of the client device502, the threshold radius 534, the area 536, and/or the merchant store530 to determine the targeted user is likely to attempt a transaction atthe merchant store 530. In such instances, the server device mayinitiate a communication 514 to the client device 502 to urge thetargeted user to pay a negative balance in the target account 510 beforeattempting to make a transaction at the merchant store 530, possiblywhere the attempted transaction may be declined. As shown, thecommunication 514 may include text indicating, “Your Account Balance isNegative.” Further, the server device may enable the targeted user toconsider making a payment using the yes or no inputs 516 in the I/Ointerface 506.

In another example, consider a scenario where the client device 502approaches the client device 504 shown in the merchant store 532 at thelocation 524. As shown, the account 512 accessed by the client device504 may be a merchant account and/or a seller account, possibly also apeer account to the target account 510. Further, consider that thetarget account 510 has a purchase history with the merchant store 532and/or the merchant account 512. Thus, the server device may retrieveand process behavioral data including purchase history data and/or thelocation data indicative of the client device 502, the threshold radius534, the derived area 536, the merchant store 532, and/or the location524 of the merchant device 504. In such instances, the server device mayinitiate the communication 514 to the client device 502 to capture thetargeted user's attention to pay off the negative balance or pay toincrease the balance in the target account 510 before attempting to makea transaction with the merchant device 504, possibly where the attemptedtransaction may be declined. Further, the server device may enable thetargeted user to consider making a payment using the yes or no inputs516 via the I/O interface 506. As demonstrated by the scenarios above,the targeted users may be contacted efficiently and also prevented fromattempting to make transactions with deficient balances. In particular,the targeted users may be contained or prevented from furtheractivities, in addition to activating or creating new accounts andperforming activities under the newly created accounts, as describedabove.

In addition, the server device may retrieve peer data including socialmedia data that indicates a link or a connection between the targetaccount 510 and the merchant account 512, possibly in a social medianetwork, a buyer-seller network, and/or a user-merchant network. In suchinstances, the server device may delay the communication 514 until themovement of the target device 502 causes the merchant device 504 to bewithin the derived area 536. Thus, the communication 514 may capture thetargeted user's attention before attempting to make at transaction withthe merchant device 504. In some embodiments, the server device mayretrieve behavioral data that includes peer data and/or social mediadata that indicates methods to contact peer accounts associated with thetarget account 510. For example, considering the scenario where thetarget account 510 and the merchant account 512 are in the same socialnetwork, the one or more data processing components of the server devicemay determine the method of contact (e.g., social media messaging) forthe target account 510 based on the determined method to contact thepeer account 512.

FIG. 6 is a flowchart of an exemplary method 600, according to anembodiment. Notably, one or more steps of the method 600 or othermethods/processes described herein may be omitted, performed in adifferent sequence, and/or combined with other methods, such as themethod 390 described above, for various types of applications.

At step 602, the method 600 may include accessing, by a server device,account data from a number of buyer and seller devices, where the numberof buyer and seller devices correspond to a number of accounts. Forexample, referring back to FIGS. 1-5, the method 600 may includeaccessing, by the server device (e.g., the server devices 102 and/or200), the account data or data packets 300 from a number of buyer andseller devices 302, 304, 306, and/or 308, where the number of the buyerand seller devices 302, 304, 306, and/or 308 correspond to a number ofaccounts 310, 314, 318, 322, 326, 330, 334, and/or 338.

At step 604, the method 600 may include segmenting, by an account engineof the server device, the number of accounts into a number of accountgroups based at least on a respective balance of each account of thenumber of accounts. For example, referring back to FIGS. 1-3A, themethod 600 may include segmenting, by an account engine (e.g., theaccount engines 112 and/or 232) of the server device described above,the number of accounts 310, 314, 318, 322, 326, 330, 334, and/or 338,into a number of account groups 342, 344, and/or 346 based on arespective balance of each account of the number of accounts 310, 314,318, 322, 326, 330, 334, and/or 338. For example, the accounts 314, 326,and/or 330 in account group 342 may have low or zero balances. Theaccounts 318 and/or 334 in the account group 344 may have moderatelynegative balances. Further, the account 310, 322, and/or 338 in theaccount group 346 may have substantially negative balances, forinstance.

At step 606, the method 600 may include determining, by the accountengine, a number of target accounts from the number of account groupsbased at least on behavioral data and/or peer account data retrievedfrom the account data. For example, referring back to FIGS. 1-3A, themethod 600 may include determining, by the account engine describedabove, a number of target accounts 310, 318, and/or 338 from the numberof account groups 344 and/or 346 based on behavioral data 312, 320,and/or 340 and/or peer account data 314, 322, 326, 330, and/or 334retrieved from the account data or data packets 300. As such, the targetaccounts 310, 318, and/or 338 may be determined or selected based oncomparing peer accounts including accounts 334 and/or 322 also shown inFIG. 3A as being included in the account groups 344 and/or 346. Inparticular, the accounts 334 and/or 322 may not be selected as targetaccounts based on a machine learning process determining that theprobability of the balances in accounts 334 and/or 322 being paid off islower that the probabilities of the target accounts 310, 318, and/or 338being paid off.

At step 608, the method 600 may include determining, by one or more dataprocessing components of the server device, a method of contact for eachtarget account based at least on the behavioral data and/or the peeraccount data. For example, referring back to FIGS. 1-3B, the method 600may include determining, by one or more data processing components ofthe server device, a method of contact from contact data 352, 366,and/or 372 for each target account 310, 318, and/or 338, respectfully,based on the behavioral data 312, 320, and/or 340, and/or the peeraccount data 314, 322, 326, 330, and/or 334. For example, the one ormore data processing components may compile the contact data 352, 366,and/or 374 from the peer account data 314, 322, 326, 330, and/or 334that includes phone number data, email address data, mailing addressdata, fax number data, and/or application programming interface (API)data, and/or other forms of data related to contacting the client device302, 304, and/or 308.

At step 610, the method 600 may include determining, by the one or moredata processing components, a respective time to communicate with arespective device for each target account based at least on the methodsof contact. For example, the method 600 may include determining, by theone or more data processing components, respective times to communicatewith respective devices 302, 304, and/or 308 for each target account310, 318, and/or 338 based at least on the methods of contact determinedfrom contact data 352, 366, and/or 372, respectfully.

At step 612, the method 600 may include initiating, by one or morecommunication components of the server device, communications to therespective devices at the respective times for each target account. Forexample, the method 600 may include initiating, by one or morecommunication components (e.g., communication component 118 and/or 238)of the server device described above, communications to the respectivedevices 302, 304, and/or 308 at the respective times determined fromtime data 354, 368, and/or 376 for each target account 310, 318, and/or338, respectively.

In some embodiments, the location data 358 and/or 380 includes sensordata received from the number of buyer and seller devices 302, 304, 306,and/or 308. For example, referring back to FIG. 5, the client device 502may include one or more sensors (e.g., proximity sensors, radios, and/orcommunication components) capable of exchanging sensor data includingradio-frequency identifier (RFID) data, near-field communications (NFC)data, geo location data, WiFi beaconing data, SSID reading data,Bluetooth data, and/or other near-field communication data. The sensordata may enable the server device to determine one or more locations ofthe client device 502. As such, the method 600 may include retrievingthe sensor data that indicates the locations of the respective devices302, 304, and/or 308 determined from the number of buyer and sellerdevices 302, 304, 306, and/or 308.

In some embodiment, the method 600 may include ranking the number oftarget accounts 310, 318, and/or 338 based on the respective balance ofeach account. For example, the target account with the most negativebalance may be ranked the highest such that a client device associatedwith the target account is one of the initial devices contacted.Further, initiating the communications to the respective devices 302,304, and/or 308 may be based on the ranking of the number of targetaccounts 310, 318, and/or 338.

FIGS. 7A and 7B illustrate an exemplary wearable computing device 700,according to an embodiment. As illustrated, the wearable computingdevice 700 may take the form of a head-mountable display (HMD) and/or anarm or wrist-mountable display. As shown in FIG. 7A, the wearablecomputing device 700 may be wearable as a HMD device. The device 700 mayinclude lenses 702 and 704. The device 700 may also include a sidecomponent 706, a side component 708, and a middle component 710. Forexample, the device 700 may be mountable on a user's head such that theside component 706 rests on one ear of the user and the side component708 rests on the other ear of the user. Further, the middle component710 may rest on the nose of the user. In some instances, the lenses 702and 704 may be positioned in front of the user's eyes. Further, thelenses 702 and 704 may include displays 712 and 714, respectively. Insome instances, the displays 712 and 714 may be transparent, partiallysee-through, and/or configured to provide an augmented reality. Further,the displays 712 and/or 714 may include touch pad displays to displaydata and receive touch inputs such that the user can manipulate graphicsprovided by the displays 712 and/or 714. The lenses 702 and/or 704 mayalso include scanners such as laser scanners configured to scan the eyesof the user to retrieve biometric inputs based on the user's eyes,retinas, and/or irises, possibly for authenticating the user's account.

As shown, the display 712 may provide a communication 730, possiblysimilar to those described above in relation to FIGS. 3A-6. As such, acommunication may be displayed, “Your Account Balance is Negative. Viewdetails?” Further, user data 732 may provide selections, “Yes” and “No.”Thus, by selecting “Yes,” possibly through verbal command or a touchselection input, the account balance may be viewed via display 712.Further, by selecting “No,” possibly through a verbal command or a touchselection input, the communication 730 may be cleared from the display712.

As shown in FIG. 7B, the wearable computing device 700 may also bewearable as an arm/wrist-mountable device. Yet, the wearable computingdevice 700 may take the form of a bracelet, an anklet, and/or anecklace, among other forms of accessories. As shown, the sidecomponents 706 and 708, the middle component 710, and/or the lenses 702and 704 may be adjustable to fit on an arm and/or wrist 724 of a user.As shown, the lens 702 may be positioned on the top of the wrist 724 tooperate as the face of a wrist watch. The side components 706, 708,and/or the middle component 710 may be adjusted to fit around the wrist724. The lens 704 may be positioned on the bottom of the wrist 724. Asshown, the lens 704 may be flexible, foldable, and/or retractable, amongother ways to adjust into the form of a wrist-watch band.

The wearable computing device 700 may include one or more sensors 716and/or 718 configured to receive a number of inputs associated with theuser. The one or more sensors 716 and/or 718 may also includeaccelerometers, gyroscopes, compasses, barometers, capacitive sensors,haptic sensors, temperature sensors, ambient light sensors, soundsensors, image sensors, biometric sensors, moisture sensors, electrodes,and/or chemical sampling sensors, among other types of sensors toreceive inputs from the user. For example, based on the way the wearablecomputing device 700 is worn as a head-mountable device or anarm/wrist-mountable device, the sensors 716 and/or 718 may be configuredto receive inputs directly and/or indirectly from the user. In someembodiments, the lens 702 may include a sensor that may, for example,include a capacitive sensor and/or proximity sensor to sense, detect,and/or identify the user of the device 700. Further, the sensor mayinclude a biometric sensor such as a fingerprint sensor. Thus, thefingerprint sensor may receive one or more fingerprint inputs from user,possibly to view an account balance.

The present disclosure, the accompanying figures, and the claims are notintended to limit the present disclosure to the precise forms orparticular fields of use disclosed. As such, it is contemplated thatvarious alternate embodiments and/or modifications to the presentdisclosure, whether explicitly described or implied herein, are possiblein light of the disclosure. Having thus described embodiments of thepresent disclosure, persons of ordinary skill in the art will recognizethat changes may be made in form and detail without departing from thescope of the present disclosure.

The invention claimed is:
 1. A non-transitory computer-readable mediumhaving stored thereon instructions executable to cause a machine toperform operations comprising: accessing, by a server device, datapackets representing account data from a plurality of user devices andmerchant devices that correspond to a plurality of accounts via anInternet communication protocol; determining, by an account engine ofthe server device, a plurality of target accounts from the plurality ofaccounts based at least on a respective number of delinquent actionsassociated with each account of the plurality of accounts; determining,by a data processing component of the server device, a combined scorefor each target account of the plurality of target accounts based onbehavioral data retrieved from the account data, wherein the combinedscore is indicative of a determined probability of reaching a targeteduser of a respective device associated with a respective target accountof the plurality of target accounts; initiating, by a communicationcomponent of the server device, a communication to the respective deviceat a respective time for each target account of the plurality of targetaccounts based on the determined combined score for each target accountof the plurality of target accounts; and after initiating thecommunication, receiving, by the server device, an authorization for afirst payment from at least one user device associated with therespective target account of the plurality of target accounts to atleast partially pay off an account balance of the respective targetaccount.
 2. The non-transitory computer-readable medium of claim 1,wherein the operations further comprise: after determining the pluralityof target accounts, determining, by the data processing component of theserver device, a method of contact for each target account of theplurality of target accounts based on the behavioral data retrieved fromthe account data; and determining, by the data processing component, therespective time to communicate with the respective device for eachtarget account of the plurality of target accounts based on the methodof contact.
 3. The non-transitory computer-readable medium of claim 1,wherein the respective number of delinquent actions associated with eachaccount of the plurality of accounts indicates a number of missedpayments associated with each account of the plurality of accounts, anumber late payments associated with each account of the plurality ofaccounts, and/or a number of rejected payments associated with eachaccount of the plurality of accounts.
 4. The non-transitorycomputer-readable medium of claim 1, wherein the operations furthercomprise ranking the plurality of target accounts based on therespective number of delinquent actions for each account of theplurality of accounts, and wherein initiating the communication to therespective device is based on the ranking of the plurality of targetaccounts.
 5. The non-transitory computer-readable medium of claim 1,wherein determining the plurality of target accounts is based onrespective balances of each account of the plurality of accounts.
 6. Thenon-transitory computer-readable medium of claim 2, wherein thebehavioral data further comprises peer data and/or social media dataindicative of methods to contact peer accounts associated with theplurality of target accounts, and wherein determining the method ofcontact for each target account of the plurality of target accounts isbased on the methods to contact peer accounts.
 7. The non-transitorycomputer-readable of claim 2, wherein the behavioral data associatedwith the plurality of target accounts indicates times associated withdeposits submitted to each target account of the plurality of targetaccounts, and wherein determining the respective time to communicatewith the respective device for each target account of the plurality oftarget accounts is based on the times associated with the depositssubmitted.
 8. The non-transitory computer-readable medium of claim 2,wherein the determining the combined score further comprises: accessinga probability score for each target account of the plurality of targetaccounts based on a probability that the respective target accountreceives a second payment; accessing a method score for each targetaccount of the plurality of target accounts based on the determinedmethod of contact for each target account of the plurality of targetaccounts; and accessing a time score for each target account of theplurality of target accounts based on the respective time determined tocommunicate with the respective device.
 9. The non-transitorycomputer-readable medium of claim 8, wherein initiating thecommunication comprises initiating an email communication, a text or SMScommunication, and/or a phone communication to the respective device atthe respective time based on the probability score, the method score,and the time score.
 10. A method, comprising: accessing, by a serverdevice, data packets representing account data from a plurality of buyerand seller devices via an Internet communication protocol, wherein theplurality of buyer and seller devices correspond to a plurality ofaccounts; determining, by an account engine of the server device, aplurality of target accounts from the plurality of accounts based onbehavioral data and/or peer account data retrieved from the accountdata; determining, by one or more data processing components of theserver device, a combined score for each target account of the pluralityof target accounts based on the behavioral data and/or peer account dataretrieved from the account data, wherein the combined score isindicative of a determined probability of reaching a targeted user of arespective device associated with a respective target account of theplurality of target accounts; initiating, by one or more communicationcomponents of the server device, a communication to the respectivedevice for each target account of the plurality of target accounts at arespective time for each target account of the plurality of targetaccounts based on the combined score determined using the behavioraldata and/or peer account data; and after initiating the communication,receiving, by the server device, an authorization for a payment from atleast one buyer device associated with the respective target account ofthe plurality of target accounts to at least partially pay off anaccount balance of the respective target account.
 11. The method ofclaim 10, further comprising: segmenting, by the account engine of theserver device, the plurality of accounts into a plurality of accountgroups based on a respective balance of each account of the plurality ofaccounts; wherein the determining the plurality of target accountsincludes determining the plurality of target accounts from the pluralityof account groups based on the behavioral data and/or peer account data.12. The method of claim 10, further comprising: after determining theplurality of target accounts, determining, by the one or more dataprocessing components of the server device, a method of contact for eachtarget account of the plurality of target accounts based on thebehavioral data and/or the peer account data; and determining, by theone or more data processing components of the server device, therespective time to communicate with the respective device for eachtarget account of the plurality of target accounts based on the methodof contact.
 13. The method of claim 10, wherein the behavioral data isassociated with the plurality of target accounts, and wherein thebehavioral data comprises location data indicative of locations of therespective devices determined from the plurality of buyer and sellerdevices, payment data indicative of times of payments made or missed forat least one of the plurality of target accounts, pattern dataindicative of patterns of payments made or missed for at least one ofthe plurality of target accounts, and/or recurring payment dataindicative of one or more recurring payments for at least one of theplurality of target accounts.
 14. The method of claim 10, furthercomprising ranking the plurality of target accounts based at least onthe respective balance of each account, and wherein initiating thecommunication to the respective devices is based at least on the rankingof the plurality of target accounts.
 15. A system, comprising: anon-transitory memory; and one or more hardware processors coupled tothe non-transitory memory and configured to read instructions from thenon-transitory memory to cause the system to perform operationscomprising: accessing data packets representing account data from aplurality of buyer and seller devices via an Internet communicationprotocol, wherein the plurality of buyer and seller devices correspondto a plurality of accounts; determining a plurality of target accountsfrom the plurality of accounts based on behavioral data and/or peeraccount data retrieved from the account data; determining a combinedscore for each target account of the plurality of target accounts basedon the behavioral data and/or peer account data retrieved from theaccount data, wherein the combined score is indicative of a determinedprobability of reaching a targeted user of a respective deviceassociated with a respective target account of the plurality of targetaccounts; initiating a communication to the respective device for eachtarget account of the plurality of target accounts at a respective timefor each target account of the plurality of target accounts based on thedetermined combined score; and after initiating the communication,receiving an authorization for a first payment from at least one buyerdevice associated with the respective target account of the plurality oftarget accounts to at least partially pay off an account balance of therespective target account.
 16. The system of claim 15, wherein theoperations further comprise: segmenting the plurality of accounts into aplurality of account groups based on a respective balance of eachaccount of the plurality of accounts; wherein the determining theplurality of target accounts includes determining the plurality oftarget accounts from the plurality of account groups based on thebehavioral data and/or peer account data.
 17. The system of claim 15,wherein the operations further comprise: after determining the pluralityof target accounts, determining a method of contact for each targetaccount of the plurality of target accounts based on the behavioral dataand/or the peer account data; and determining the respective time tocommunicate with the respective device for each target account of theplurality of target accounts based on the method of contact.
 18. Thesystem of claim 15, wherein a respective number of delinquent actionsassociated with each account of the plurality of accounts indicates anumber of missed payments associated with each account of the pluralityof accounts, a number of late payments associated with each account ofthe plurality of accounts, and/or a number of rejected paymentsassociated with each account of the plurality of accounts.
 19. Thesystem of claim 17, wherein the behavioral data further comprises dataindicative of methods to contact peer accounts associated with theplurality of target accounts, and wherein determining the method ofcontact for each target account of the plurality of target accounts isbased on the methods to contact peer accounts.
 20. The system of claim17, wherein the behavioral data associated with the plurality of targetaccounts indicates times associated with deposits submitted to eachtarget account of the plurality of target accounts, and whereindetermining the respective time to communicate with the respectivedevice for each target account is based on the times associated with thedeposits submitted.